Screening candidate diagnostic biomarkers for diabetic kidney disease.

Autor: Huang X; Department of Clinical Laboratory, the First Affiliated Hospital of Kunming Medical University, Kunming, China.; Yunnan Key Laboratory of Laboratory Medicine, Kunming, China.; Yunnan Innovation Team of Clinical Laboratory and Diagnosis, First Affiliated Hospital of Kunming Medical University, Kunming, China., Zhang H; Department of Clinical Laboratory, the First Affiliated Hospital of Kunming Medical University, Kunming, China.; Yunnan Key Laboratory of Laboratory Medicine, Kunming, China.; Yunnan Innovation Team of Clinical Laboratory and Diagnosis, First Affiliated Hospital of Kunming Medical University, Kunming, China., Liu J; Department of Clinical Laboratory, the Third People's Hospital of Kunming, Kunming, China., Yang X; Department of Clinical Laboratory, the People's Hospital of ChuXiong Yi Autonomous Prefecture, ChuXiong, China., Liu Z; Department of Clinical Laboratory, the First Affiliated Hospital of Kunming Medical University, Kunming, China.; Yunnan Key Laboratory of Laboratory Medicine, Kunming, China.; Yunnan Innovation Team of Clinical Laboratory and Diagnosis, First Affiliated Hospital of Kunming Medical University, Kunming, China.
Jazyk: angličtina
Zdroj: Journal of clinical laboratory analysis [J Clin Lab Anal] 2024 Feb; Vol. 38 (3), pp. e25000. Date of Electronic Publication: 2024 Feb 01.
DOI: 10.1002/jcla.25000
Abstrakt: Background: There are big differences in treatments and prognosis between diabetic kidney disease (DKD) and non-diabetic renal disease (NDRD). However, DKD patients couldn't be diagnosed early due to lack of special biomarkers. Urine is an ideal non-invasive sample for screening DKD biomarkers. This study aims to explore DKD special biomarkers by urinary proteomics.
Materials and Methods: According to the result of renal biopsy, 142 type 2 diabetes mellitus (T2DM) patients were divided into 2 groups: DKD (n = 83) and NDRD (n = 59). Ten patients were selected from each group to define urinary protein profiles by label-free quantitative proteomics. The candidate proteins were further verifyied by parallel reaction monitoring (PRM) methods (n = 40). Proteins which perform the same trend both in PRM and proteomics were verified by enzyme-linked immunosorbent assays (ELISA) with expanding the sample size (n = 82). The area under the receiver operating characteristic curve (AUC) was used to evaluate the accuracy of diagnostic biomarkers.
Results: We identified 417 peptides in urinary proteins showing significant difference between DKD and NDRD. PRM verification identified C7, SERPINA4, IGHG1, SEMG2, PGLS, GGT1, CDH2, CDH1 was consistent with the proteomic results and p < 0.05. Three potential biomarkers for DKD, C7, SERPINA4, and gGT1, were verified by ELISA. The combinatied SERPINA4/Ucr and gGT1/Ucr (AUC = 0.758, p = 0.001) displayed higher diagnostic efficiency than C7/Ucr (AUC = 0.632, p = 0.048), SERPINA4/Ucr (AUC = 0.661, p = 0.032), and gGT1/Ucr (AUC = 0.661, p = 0.029) respectively.
Conclusions: The combined index SERPINA4/Ucr and gGT1/Ucr can be considered as candidate biomarkers for diabetic nephropathy after adjusting by urine creatinine.
(© 2024 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC.)
Databáze: MEDLINE
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